{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/gyyang/neurogym/blob/master/examples/demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "id": "4zW6CU8F69Zp"
   },
   "source": [
    "## Exploring NeuroGym tasks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "xIE7qx_a7D0e"
   },
   "source": [
    "NeuroGym is a comprehensive toolkit that allows training any network model on many established neuroscience tasks using Reinforcement Learning techniques. It includes working memory tasks, value-based decision tasks and context-dependent perceptual categorization tasks.\n",
    "\n",
    "In this notebook we first show how to install the relevant toolbox.\n",
    "\n",
    "We then show how to access the available tasks and their relevant information.\n",
    "\n",
    "Finally we train an LSTM network on the Random Dots Motion task using the A2C algorithm [Mnih et al. 2016](https://arxiv.org/abs/1602.01783) implemented in the [stable-baselines3](https://stable-baselines3.readthedocs.io/en/master/) toolbox, and plot the results.\n",
    "\n",
    "You can easily change the code to train a network on any other available task or using a different algorithm (e.g. ACER, PPO2).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3YKMoHkR2xL"
   },
   "source": [
    "### Installation on google colab\n",
    "\n",
    "Uncomment and execute cell below if running on google colab.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "hidden": true,
    "id": "Mp-K8wKGtBoE",
    "outputId": "9e2cefb9-2b67-4a3b-e838-5d60749b4b6f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%tensorflow_version` not found.\n"
     ]
    }
   ],
   "source": [
    "# # Install gymnasium\n",
    "# ! pip install gymnasium\n",
    "# # Install neurogym\n",
    "# ! git clone https://github.com/neurogym/neurogym.git\n",
    "# %cd neurogym/\n",
    "# ! pip install -e .\n",
    "# # Install stable-baselines3\n",
    "# ! pip install stable-baselines3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZllQKETBVXNM"
   },
   "source": [
    "### Explore tasks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "CehSnXXBVMsh",
    "outputId": "06b18bf8-bfaa-4147-bf82-434665284741"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AntiReach-v0\n",
      "Bandit-v0\n",
      "ContextDecisionMaking-v0\n",
      "DawTwoStep-v0\n",
      "DelayComparison-v0\n",
      "DelayMatchCategory-v0\n",
      "DelayMatchSample-v0\n",
      "DelayMatchSampleDistractor1D-v0\n",
      "DelayPairedAssociation-v0\n",
      "DualDelayMatchSample-v0\n",
      "EconomicDecisionMaking-v0\n",
      "GoNogo-v0\n",
      "HierarchicalReasoning-v0\n",
      "IntervalDiscrimination-v0\n",
      "MotorTiming-v0\n",
      "MultiSensoryIntegration-v0\n",
      "Null-v0\n",
      "OneTwoThreeGo-v0\n",
      "PerceptualDecisionMaking-v0\n",
      "PerceptualDecisionMakingDelayResponse-v0\n",
      "PostDecisionWager-v0\n",
      "ProbabilisticReasoning-v0\n",
      "PulseDecisionMaking-v0\n",
      "Reaching1D-v0\n",
      "Reaching1DWithSelfDistraction-v0\n",
      "ReachingDelayResponse-v0\n",
      "ReadySetGo-v0\n",
      "SingleContextDecisionMaking-v0\n",
      "psychopy.RandomDotMotion-v0\n",
      "psychopy.SpatialSuppressMotion-v0\n",
      "psychopy.VisualSearch-v0\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "import gymnasium as gym\n",
    "import neurogym as ngym\n",
    "from neurogym.utils import info, plotting\n",
    "warnings.filterwarnings('ignore')\n",
    "info.all_tasks()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jzF5leN1R2xU"
   },
   "source": [
    "### Visualize a single task\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "RaH9CcJdHY5G",
    "outputId": "b55de485-603f-4f73-af74-8cfecdcd3301"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<OrderEnforcing<PassiveEnvChecker<GoNogo>>>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task = 'GoNogo-v0'\n",
    "env = gym.make(task)\n",
    "print(env)\n",
    "# plotting.plot_env(env, num_steps=300, def_act=0, ob_traces=['Fixation cue', 'Stim1', 'Stim2'], fig_kwargs={'figsize': (12, 12)})\n",
    "fig = plotting.plot_env(\n",
    "    env,\n",
    "    num_steps=100,\n",
    "    # def_act=0,\n",
    "    ob_traces=['Fixation cue', 'NoGo', 'Go'],\n",
    "    # fig_kwargs={'figsize': (12, 12)}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h5BdRE6vVjeS"
   },
   "source": [
    "### Explore wrappers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "OqyGbBGRVlhK",
    "outputId": "27a2e15b-5414-4a08-c6db-f66a7fc78b95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monitor-v0\n",
      "Noise-v0\n",
      "PassAction-v0\n",
      "PassReward-v0\n",
      "RandomGroundTruth-v0\n",
      "ReactionTime-v0\n",
      "ScheduleAttr-v0\n",
      "ScheduleEnvs-v0\n",
      "SideBias-v0\n",
      "TrialHistoryV2-v0\n"
     ]
    }
   ],
   "source": [
    "info.all_wrappers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "k92VZ01IN5bs",
    "outputId": "391cb8e3-36d6-47e7-e71e-166207552475"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'### TrialHistoryV2-v0\\n\\nLogic: Missing description\\n\\n\\n#### Source code #### \\n\\nclass TrialHistoryV2(TrialWrapper):\\n    \"\"\"Change ground truth probability based on previous outcome.\\n\\n    Args:\\n        probs: matrix of probabilities of the current choice conditioned\\n            on the previous. Shape, num-choices x num-choices\\n    \"\"\"\\n\\n    def __init__(self, env, probs=None):\\n        super().__init__(env)\\n        try:\\n            self.n_ch = len(self.choices)  # max num of choices\\n        except AttributeError:\\n            raise AttributeError(\\n                \"TrialHistory requires task to \" \"have attribute choices\"\\n            )\\n        if probs is None:\\n            probs = np.ones((self.n_ch, self.n_ch)) / self.n_ch  # uniform\\n        self.probs = probs\\n        assert self.probs.shape == (self.n_ch, self.n_ch), (\\n            \"probs shape wrong, should be\" + str((self.n_ch, self.n_ch))\\n        )\\n        self.prev_trial = self.rng.choice(self.n_ch)  # random initialization\\n\\n    def new_trial(self, **kwargs):\\n        if \"probs\" in kwargs:\\n            probs = kwargs[\"probs\"]\\n        else:\\n            probs = self.probs\\n        p = probs[self.prev_trial, :]\\n        # Choose ground truth and update previous trial info\\n        self.prev_trial = self.rng.choice(self.n_ch, p=p)\\n        ground_truth = self.choices[self.prev_trial]\\n        kwargs.update({\"ground_truth\": ground_truth, \"probs\": probs})\\n        return self.env.new_trial(**kwargs)\\n\\n\\n'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info.info_wrapper('TrialHistoryV2-v0', show_code=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OCFMPbzX38Wj"
   },
   "source": [
    "### Train a network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "code_folding": [],
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 853
    },
    "colab_type": "code",
    "id": "jAxTPbzL38Wl",
    "outputId": "3405476e-8c1b-4aa6-a45a-4206872cb732"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/envs/registration.py:481: UserWarning: \u001b[33mWARN: The environment creator metadata doesn't include `render_modes`, contains: ['paper_link', 'paper_name', 'tags']\u001b[0m\n",
      "  logger.warn(\n",
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.choices to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.choices` for environment variables or `env.get_wrapper_attr('choices')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n",
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.rng to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.rng` for environment variables or `env.get_wrapper_attr('rng')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n",
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.new_trial to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.new_trial` for environment variables or `env.get_wrapper_attr('new_trial')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "--------------------\n",
      "Number of steps:  10000.0\n",
      "Average reward:  0.27335\n",
      "--------------------\n",
      "--------------------\n",
      "Number of steps:  20000.0\n",
      "Average reward:  0.2262\n",
      "--------------------\n",
      "--------------------\n",
      "Number of steps:  30000.0\n",
      "Average reward:  0.20145\n",
      "--------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2300      |\n",
      "|    iterations         | 100000    |\n",
      "|    time_elapsed       | 217       |\n",
      "|    total_timesteps    | 500000    |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.000595 |\n",
      "|    explained_variance | -6.48     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 99999     |\n",
      "|    policy_loss        | -1.45e-06 |\n",
      "|    value_loss         | 0.000583  |\n",
      "-------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "from neurogym.wrappers import monitor, TrialHistoryV2\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "from stable_baselines3 import A2C  # ACER, PPO2\n",
    "warnings.filterwarnings('default')\n",
    "# task paremters\n",
    "timing = {'fixation': ('constant', 300),\n",
    "          'stimulus': ('constant', 700),\n",
    "          'decision': ('constant', 300)}\n",
    "kwargs = {'dt': 100, 'timing': timing}\n",
    "# wrapper parameters\n",
    "n_ch = 2\n",
    "p = 0.8\n",
    "num_blocks = 2\n",
    "probs = np.array([[p, 1-p], [1-p, p]])  # repeating block\n",
    "\n",
    "# build task\n",
    "env = gym.make(task, **kwargs)\n",
    "# Apply the wrapper\n",
    "env = TrialHistoryV2(env, probs=probs)\n",
    "env = monitor.Monitor(env, folder='content/tests/', sv_per=10000, verbose=1, sv_fig=True, num_stps_sv_fig=100)\n",
    "# the env is now wrapped automatically when passing it to the constructor\n",
    "env = DummyVecEnv([lambda: env])\n",
    "model = A2C(\"MlpPolicy\", env, verbose=1, policy_kwargs={'net_arch': [64, 64]})\n",
    "model.learn(total_timesteps=500000, log_interval=100000)\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "svUQlptJAVv9"
   },
   "source": [
    "### Visualize results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/envs/registration.py:481: UserWarning: \u001b[33mWARN: The environment creator metadata doesn't include `render_modes`, contains: ['paper_link', 'paper_name', 'tags']\u001b[0m\n",
      "  logger.warn(\n",
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.choices to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.choices` for environment variables or `env.get_wrapper_attr('choices')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n",
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.rng to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.rng` for environment variables or `env.get_wrapper_attr('rng')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n",
      "/Users/giuliacrocioni/miniforge3/envs/neurogym/lib/python3.10/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.new_trial to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.new_trial` for environment variables or `env.get_wrapper_attr('new_trial')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Create task\n",
    "env = gym.make(task, **kwargs)\n",
    "# Apply the wrapper\n",
    "env = TrialHistoryV2(env, probs=probs)\n",
    "env = DummyVecEnv([lambda: env])\n",
    "fig = plotting.plot_env(\n",
    "    env,\n",
    "    num_steps=100,\n",
    "    # def_act=0,\n",
    "    ob_traces=['Fixation cue', 'NoGo', 'Go'],\n",
    "    # fig_kwargs={'figsize': (12, 12)},\n",
    "    model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "example_neurogym_rl.ipynb",
   "provenance": []
  },
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
